





"""simple docstring"""


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor






class 							lowercase__			(   unittest.TestCase	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A=1_3						,   _A=3						,   _A=2_2_4						,   _A=3_0						,   _A=4_0_0						,   _A=True						,   _A=None						,   _A=True						,   _A=[0.5, 0.5, 0.5]						,   _A=[0.5, 0.5, 0.5]						,   ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			size if size is not None else {"""height""": 1_8, """width""": 1_8}
									UpperCamelCase				:		int				   =			parent
									UpperCamelCase				:		Any				   =			batch_size
									UpperCamelCase				:		List[str]				   =			num_channels
									UpperCamelCase				:		List[str]				   =			image_size
									UpperCamelCase				:		Optional[int]				   =			min_resolution
									UpperCamelCase				:		List[str]				   =			max_resolution
									UpperCamelCase				:		List[Any]				   =			do_resize
									UpperCamelCase				:		int				   =			size
									UpperCamelCase				:		Optional[Any]				   =			do_normalize
									UpperCamelCase				:		int				   =			image_mean
									UpperCamelCase				:		int				   =			image_std







			def   _a      (      self    ):






									'''simple docstring'''







									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}






@require_torch
@require_vision
class 							lowercase__			(   __SCREAMING_SNAKE_CASE					,     unittest.TestCase	):

			"""simple docstring"""
			__lowerCAmelCase     :				str										=  ViTImageProcessor if is_vision_available() else None







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Dict				   =			EfficientFormerImageProcessorTester(self    )







			@property
			def   _a      (      self    ):






									'''simple docstring'''







									return self.image_proc_tester.prepare_image_processor_dict()







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[Any]				   =			self.image_processing_class(**self.image_processor_dict    )
									self.assertTrue(hasattr(_A						,   """image_mean"""    )    )
									self.assertTrue(hasattr(_A						,   """image_std"""    )    )
									self.assertTrue(hasattr(_A						,   """do_normalize"""    )    )
									self.assertTrue(hasattr(_A						,   """do_resize"""    )    )
									self.assertTrue(hasattr(_A						,   """size"""    )    )







			def   _a      (      self    ):






									'''simple docstring'''







									pass







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									UpperCamelCase				:		Any				   =			prepare_image_inputs(self.image_proc_tester						,   equal_resolution=_A    )
									for image in image_inputs:
															self.assertIsInstance(_A						,   Image.Image    )

									# Test not batched input
									UpperCamelCase				:		Optional[int]				   =			image_processor(image_inputs[0]						,   return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape						,   (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    )						,   )

									# Test batched
									UpperCamelCase				:		Union[str, Any]				   =			image_processor(_A						,   return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape						,   (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    )						,   )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Optional[Any]				   =			self.image_processing_class(**self.image_processor_dict    )
									# create random numpy tensors
									UpperCamelCase				:		List[Any]				   =			prepare_image_inputs(self.image_proc_tester						,   equal_resolution=_A						,   numpify=_A    )
									for image in image_inputs:
															self.assertIsInstance(_A						,   np.ndarray    )

									# Test not batched input
									UpperCamelCase				:		Any				   =			image_processor(image_inputs[0]						,   return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape						,   (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    )						,   )

									# Test batched
									UpperCamelCase				:		Tuple				   =			image_processor(_A						,   return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape						,   (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    )						,   )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									UpperCamelCase				:		Union[str, Any]				   =			prepare_image_inputs(self.image_proc_tester						,   equal_resolution=_A						,   torchify=_A    )
									for image in image_inputs:
															self.assertIsInstance(_A						,   torch.Tensor    )

									# Test not batched input
									UpperCamelCase				:		Any				   =			image_processor(image_inputs[0]						,   return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape						,   (
									        1,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    )						,   )

									# Test batched
									UpperCamelCase				:		Any				   =			image_processor(_A						,   return_tensors="""pt"""    ).pixel_values
									self.assertEqual(
									    encoded_images.shape						,   (
									        self.image_proc_tester.batch_size,
									        self.image_proc_tester.num_channels,
									        self.image_proc_tester.size["""height"""],
									        self.image_proc_tester.size["""width"""],
									    )						,   )









"""simple docstring"""




import warnings

from .generation import TFGenerationMixin






class        __magic_name__				(			UpperCAmelCase__							):






				'''simple docstring'''
				# warning at import time
				warnings.warn(
				    "Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
				    "be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead."		,   UpperCAmelCase__		,   )



